This study approaches typhoon landfalls as exogenous climate risk events, designating the moment of landfall as the critical intervention point. Utilizing the difference-in-differences (DID) methodology, the research examines the influence of typhoon disasters on the stock returns of publicly traded companies in China, and assesses how financial risks propagate through supply chain networks triggered by typhoon disasters. To gain a more nuanced understanding of these effects, the paper engages in a detailed mechanism analysis by examining the intensity of digital transformation. The results suggest that typhoon disasters have a significant and detrimental impact on the stock returns of firms located in affected areas, with this effect rippling through to their suppliers and customers via the intricate web of supply chain connections. Moreover, the study uncovers a distinct asymmetry in the spillover effects between suppliers and customers. Specifically, the research highlights that the level of digital transformation is instrumental in alleviating the financial risks associated with typhoons and serves as a protective barrier against the adverse effects on stock returns. Finally, a comprehensive suite of robustness checks reinforces the validity and reliability of the study’s conclusions.

The fundamental methodologies of machine learning and econometrics are reviewed. We also discuss the challenges of integrating the data-driven and model-based causal approaches and conjecture how it may yield new insights to empirical economic studies.

Digital finance, as one of the “five major areas” of finance, plays a crucial role in improving the efficiency of financial services, promoting inclusive finance, and empowering high-quality economic development. Based on relevant literature from core English and Chinese-language databases spanning 2014-2025, this paper constructs a research framework for digital finance using bibliometrics and qualitative analysis, analyzes the shortcomings of existing research, and proposes potential future research directions. The results show that: First, Chinese-language literature focuses on policy guidance and local practices, while English-language literature focuses on sustainable development and global comparisons. Second, the research framework for digital finance can be systematically summarized through a logical thread of “measurement indicators, influencing factors, economic and social effects, innovative practices”. Third, future research can focus on refining measurement indicators, accurately identifying influencing factors, comprehensively addressing economic and social effects, and advancing the research on innovative practices. This paper broadens the perspective for further research on digital finance and provides insights for promoting its high-quality development in practice.

With the increasing demand for rural delivery in mountainous areas, the routing problem of rural delivery logistics in mountainous areas (RPRDLMA) has become an academic hotspot. Based on the background of rural passenger, cargo and postal integration development, the RPRDLMA under the cooperative distribution of bus-electric vehicle-drone (RPRDLMA-CDBEVD) is studied in this paper. Firstly, the village service points are divided into type TC and type FC, meaning that they are served by EVs or by drones, according to their geographic location, distribution characteristics and volume of cargo delivered or mailed. Next, a continuous function of bus idle capacity is established based on the tidal rural passenger flow characteristics. Then, the RPRDLMA-CDBEVD model is constructed with the goal of total cost minimization. Specifically, the total cost includes commissioning cost, capacitybased cost, distance-based cost, time-based cost and electricity consumption cost. In order to solve the model, a hybrid algorithm of multi-constraint modified clustering algorithm and improved adaptive genetic algorithm (MCDCA-IAGA) is designed. The experimental results and case studies show that the collaborative delivery mode of passenger shuttle bus electric vehicle unmanned aerial vehicle effectively reduces delivery costs by 2.9% and delivery time by 8.6%, providing a feasible solution for logistics path planning in mountainous and rural areas.

Individual selection preferences and the abuse of recommendation algorithms have trapped the public in an information cocoon dilemma. It would trigger differentiated collective behavior, exacerbate the formation of opinion polarization, and even have a serious impact on social public order. In this paper, we systematically analyze the effects of differences in public behavior within the information cocoon on the interaction between heterogeneous opinion groups, including the intra-group homogeneity restriction weakening-strengthening effect and the inter-group inhibition-promotion combination interaction effect. Then, based on the Lotka-Volterra modeling approach, the opinion polarization dynamic model with the interaction of heterogeneous opinions is constructed. Besides, the equilibrium points and their stabilities are estimated, too. Moreover, we also explore the law of opinion polarization through numerical simulations and empirical analysis. The results show that under the influence of the information cocoon, the weaker the intra-group homogeneity restriction and the stronger the inter-group promotion effect, the faster and the larger the expansion of the two groups, and the more likely to generate binary polarization situation. What's more, when the inter-group inhibition effect is stronger and the intra-group homogeneous restriction of heterogeneous opinion is weaker, the expansion rate of the group would slow down and the size would decrease and even disappear after reaching the peak, and generate single polarization situation. In addition, the potential diffusion range positively affects the expansion rate and final size of the group itself. Furthermore, the potential diffusion range would also slow down the expansion of the heterogeneous group under the inter-group promotion effect, but does not affect its final size.

The rapid expansion of shared mobility services has introduced innovative solutions for urban transportation. Ride-hailing platforms, facilitated through user-friendly smartphone applications, seamlessly connect individual preferences with immediate vehicle availability. In carpooling services, passengers can share a ride in the same vehicle, setting their respective destinations as waypoints to increase vehicle utilization. This study proposes a carpooling and dispatching model for ride-hailing services based on a self-attention reinforcement learning network. Initially, a carpooling travel topology network is constructed, considering factors like passenger pick-up and drop-off times and locations. An on-demand algorithm is designed to identify ride-hailing orders suitable for carpooling. Subsequently, a self-attention reinforcement learning network is employed for order dispatching optimization. Through the implementation of policy gradient techniques for learning and training, the integration of masking methods ensures the efficacy of order dispatching. Leveraging the strengths of “offline training & online decision-making”, the proposed strategy tackles the challenges of enhancing the real-time responsiveness of large-scale ride-hailing dispatching services. Finally, the real-world case study is conducted based on ride-hailing orders in Beijing, China. Results underscore the efficiency of the order dispatching algorithm in achieving near-optimal route selections while reaching a real-time demand response. Although carpooling slightly increases passenger waiting times, it significantly boosts ride-hailing operational efficiency, alleviates traffic congestion, and mitigates environmental pollutants.

Climate change has emerged as a new source of instability in the global financial system, making the scientific identification and assessment of its transmission channels to the financial sector a critical issue in the field of climate finance. Currently, climate-related financial risk modeling and practical applications still face numerous obstacles. In this context, this paper reviews several key developments in climate-related financial risk studies, including the characteristics of climate risks in financial markets, the methodologies and practices for assessing climate financial risks, and future research directions. To be specific, this study first elaborates on three crucial features of climate financial risks. Second, it systematically reviews three streams of approaches for climate financial risk assessment developed in recent years, analyzes their applicability and limitations, and examines relevant practices adopted by central banks and financial regulators across different countries. Finally, the paper identifies promising directions for future research to support both theoretical advancement and practical implementation in the field of climate financial risk assessment.

The environment of international politics and economics is becoming increasingly complex and ever-changing, posing great challenges to the resilience and security of industry chains and supply chains. As an important part in supply chain management, procuring decisions are now influenced by various uncertain factors (such as supply disruption, transportation disruption, price volatility), thus directly affecting the cost of enterprises and the resilience of supply chains. This paper provides a review of the resilient supplier selection and order allocation problem, providing a basic description and a general framework for the problem. Especially, this paper focuses on different aspects (such as the four different types of risk and the corresponding modeling, the risk response strategies, the three mainstream mathematical modeling methods, commonly considered factors, and the solving algorithms etc) to review the problem. Finally, this paper states insights into future research trends.

This paper examines the impact of digital inclusive finance on the operational performance of regional commercial banks using a fixed-effects model based on balanced panel data from 78 urban and rural commercial banks spanning from 2011 to 2021. The results indicate a significant negative relationship between the two. This conclusion remains valid after addressing endogeneity issues and conducting robustness tests, suggesting that the current competitive crowding-out effect still exerts a substantial influence. Further analysis through moderation and threshold effects reveal that the technology spillover effect of digital inclusive finance drives business innovation and enhances risk-taking capacity among regional commercial banks, thereby mitigating their negative effects, with the moderation effect on risk-taking being more pronounced. The threshold parameter estimates show that business innovation has a more significant negative convergence moderation effect on rural commercial banks, while risk-taking exhibits a more significant negative convergence moderation effect on urban commercial banks. The findings of this study provide important practical insights for the digital transformation of regional commercial banks and the sustainable and healthy development of regional economies.

This paper provides a classification framework to integrate multi-source information, including available platform firm data, survey data, and industry reports, to estimate the size of platform employment. We first define and conceptualize platform employment, which is then classified into cloud-based and location-based platform employment. We then estimate the total size and distribution of platform employment in 2023, and correct for duplicates arising from workers engaged on multiple platforms. We also estimate full-time and part-time workers separately. The results show that the total number of platform workers in China has reached 247 million in 2023, including approximately 118 million part-time and 129 million full-time workers, the latter accounts for 14.9% of China’s working-age population. Notably, cloud-based platforms employ significantly more workers than location-based platforms, with cloud-based roles predominantly part-time whereas location-based positions are primarily full-time. The paper provides a consistent framework for incorporating updated information from various sources to provide timely estimates of China’s platform workforce.

Small and medium-sized enterprises (SMEs) in supply chains often face significant financing difficulties, which hinder their high-quality development. Block-chain technology-driven third-party financial service platforms offer a new approach to solving this issue. This paper explores the government's regulatory behavior strategy, the third-party financial service platform's blockchain information sharing behavior strategy, and the small and medium-sized enterprises' financing integrity behavior strategy by constructing a tripartite evolutionary game model of “government-third-party financial service platform-SMEs". It conducts an analysis on the stability of the equilibrium points in the tripartite evolutionary game and discusses the impact of blockchain technology cost, government regulatory cost, government reward and punishment intensity, and enterprise income on the equilibrium of the tripartite evolutionary game through parameter sensitivity analysis. The results show that: 1) Whether a third-party financial service platform chooses to share information through blockchain depends not only on the cost of blockchain technology but also on the government's rewards and punishments for the platform and small and medium-sized enterprises (SMEs), as well as the size of the returns from default risks. 2) Conventional wisdom holds that digital supply chain finance driven by blockchain is inevitably superior to traditional supply chain finance. However, this study finds that only when the government dynamically rewards and punishes platforms to improve the transparency of supply chain financial information and constrains enterprises to reduce financing default rates under specific circumstances, will the financing efficiency of blockchain supply chain finance surpass that of traditional supply chain finance.

The 20th National Congress of the Communist Party of China emphasized promoting common prosperity through high-quality development, but the the rural-urban income disparity is still relatively large. The adoption and diffusion of artificial intelligence, a major general-purpose technology represented by robots, has a profound impact on the labor market and income distribution. Based on the characteristic facts of China’s urban-rural dual economic structure, using the IFR and provincial panel data from 2005 to 2020, as well as the data of CLDS in 2014 and 2016, this paper empirically analyzes the impact of robot application on urban-rural income gap and its internal mechanism from the macro and micro levels. The results show the application of industrial robots and the income gap between urban and rural areas presents an inverted U-shaped trend, which increases first and then decreases. From a micro perspective, the growth rates of total income and wage income for rural residents are higher than those of urban residents, but this impact is mainly concentrated in the eastern regions. This papar not only provides policy reference for the government to promote the development of artificial intelligence technology, industrial structure upgrading and high-quality economic development, but also provides policy enlightenment for narrowing the urban-rural income gap, rural revitalization and common prosperity.

The rapid development of artificial intelligence has profoundly reshaped both the substantive focus and the methodological paradigms of behavioral science. This paper systematically synthesizes three frontier research directions that have emerged from these changes: 1) Investigations of human attitudes and behaviors during interactions with AI, and the mechanisms through which AI affects human decision-making and preferences; 2)behavioral experimental studies examining the behavioral characteristics and preference patterns of large language models; 3) methodological innovations enabled by AI technologies, including the use of AI agents as surrogates for human participants in surveys and experiments, and complex-systems research that builds dynamic interactive systems based on multi-agent frameworks. The paper concludes with a discussion of the challenges confronting, and future directions for, interdisciplinary research at the intersection of AI and behavioral science.

As an emerging economic community with distinctive competitive advantages, the platform ecosystem has garnered significant attention in academic research. However, due to differences in research perspectives and contexts, a clear and unified theoretical framework has yet to be established. Based on bibliometric analysis and a systematic literature review, this study examines the existing literature on platform ecosystems, clarifies the concept and characteristics of platform ecosystems, summarizes the theoretical framework, and explores potential future research directions. The study first identifies that platform ecosystems encompass core elements such as modular architecture, value propositions, and ecosystem governance, alongside characteristics such as modular complementarity, non-hierarchical control, multi-party interactions, and network effects. It then constructs a theoretical framework for platform ecosystems, specifically elaborating on the foundational roles of modular architecture and value propositions, the governance structure formed by open access, power distribution, and benefit-sharing mechanisms, and value co-creation driven by mechanisms, processes, and value capture. Finally, based on the theoretical framework, the study proposes future research directions, aiming to provide valuable insights for both theoretical research on platform ecosystems and practical management applications.

Short video marketing has become a core scenario for e-commerce conversion. While accelerated information dissemination and information overload have reduced consumer patience thresholds, the non-linear mechanisms through which video duration affects sales conversion remain unclear. This study examines the inverted U-shaped relationship between short video duration and product sales from the perspectives of information effectiveness and overload, revealing how this relationship is moderated by product attributes (price and type). Key findings include: 1) Information effectiveness (positive mediation) and information overload (negative mediation)constitute dual mediation pathways that jointly drive the inverted U-shaped relationship; 2) Higher-priced products reduce the optimal duration threshold and strengthen the relationship, whereas experiential products exhibit lower thresholds and weaker relationships compared to search-oriented products. The research establishes a dynamic trade-off framework for digital content marketing, demonstrating that the interplay between information density and cognitive load determines duration threshold effects. These conclusions provide empirical support for optimizing platform algorithms and innovating content production paradigms.

Data elements are a new driving force for innovation in high-tech industries in the digital age, and market integration is an external environmental support for promoting innovation in high-tech industries. It is necessary to study the impact of data elements and market integration on innovation in high-tech industries within the same framework. On the basis of theoretical analysis, this paper conducts empirical research based on China’s provincial panel data, and comprehensively uses panel regression model, mesomeric effect model and panel threshold model to study the impact of data element development and market integration on high-tech industrial innovation. The results show that: 1) The development of data elements has a significant positive impact on innovation in high-tech industries, and market integration has strengthened the innovation driving effect of data elements. 2) Market integration has a significant mesomeric effect, and data elements can drive high-tech industrial innovation by promoting market integration. 3) The threshold effect indicates that as the threshold value of market integration increases, the impact of data elements on high-tech industry innovation shows a trend of first increasing and then decreasing. When the market integration is at a moderate level, it is more conducive to unleashing the innovation driving effect of data elements. 4) Multidimensional regression analysis shows that the positive impact of data element development on high-tech industry innovation in the central region is significantly greater than that in the eastern and western regions. Compared with medium-sized enterprises, data element development has a stronger driving effect on the innovation of large high-tech enterprises. Therefore, high-tech enterprises should continuously enhance their ability to mine, apply, and transform data elements. Relevant government departments should pay attention to the development and utilization, efficient circulation, and ownership protection of data elements, actively cultivate a unified and standardized data element market, promote regional collaborative development of data elements, and strengthen the innovation driving effect of data elements.

Technological innovation is an important breakthrough for realizing a strong manufacturing country and building a modern industrial system. China issued a strategic document on comprehensively promoting the implementation of intelligent manufacturing in May 2015. Intelligent manufacturing policy is a key institutional arrangement that promotes the transformation and upgrading of manufacturing enterprises and enhances the global competitiveness of manufacturing supply chains. It is important for enhancing the resilience and security level of the supply chain to explore how the intelligent manufacturing policy promotes the digital innovation of manufacturing enterprises by increasing their profits from intelligent production, easing their financing constraints, enhancing the efficiency of supply chain collaboration and increasing the government’s subsidies so as to realize the digital transformation of the supply chain of manufacturing enterprises. This paper constructs an evolutionary game model of government-bank-manufacturing enterprises-distribution enterprises, and based on the data of Chinese A-share listed companies from 2007 to 2022, it empirically tests the mechanism and effect of the intelligent manufacturing policy on technological innovation of manufacturing enterprises from the perspective of the supply chain of manufacturing enterprises by using a DID model. It has been found that the intelligent manufacturing policy can effectively incentivize firms to choose technological innovation strategies while further strengthening active cooperation with distribution firms. Mechanism analysis finds that the intelligent manufacturing policy promotes technological innovation in manufacturing firms mainly by increasing government subsidies, lowering interest rates of innovation and increasing corporate profits. Heterogeneity analysis shows that the intelligent manufacturing policy effectively promotes technological innovation in state-owned enterprises, non-eastern enterprises and large-scale enterprises, but is detrimental to the development of technological innovation in private enterprises, eastern enterprises and small enterprises. This paper contributes to a comprehensive understanding of the micro-mechanism and differentiated effects of the intelligent manufacturing policy, provides a reliable basis for optimizing the intelligent manufacturing policy system and boosting the development of technological innovation, and is also an important reference and guidance for the current in-depth promotion of the digital transformation of the manufacturing enterprises.

To solve the problem of instability and imprecision of carbon price prediction with single information source, single decomposition technology and single prediction method, a hybrid prediction model of carbon price based on multi-source data feature and multi-scale analysis is proposed, called CPS-MEMD-SVR-MLR. 1) Multi-source data analysis: This effectively integrates historical carbon trading prices related to carbon prices, macroeconomic development levels, fossil energy prices, exchange rates, and social media sentiment data based on news text information; 2) Multi-scale analysis: This uses multiple empirical mode decomposition technology (MEMD) to decompose multi-source data into prediction features under different modes; 3) Hybrid prediction analysis: This uses fuzzy entropy theory to orderly integrate econometric model and machine learning models, and then integrates the predicted values of each mode into the final result. This paper takes the carbon price of the European Union (EU) from February 11, 2015 to February 27, 2023 as a case study. Based on seven scenarios and DM tests, the results show that: 1) The prediction accuracy of the hybrid model proposed in this paper is better than other comparison models; 2) Social media sentiment can improve the prediction accuracy of carbon price, and it is better than the single factor prediction; 3) The introduction of MEMD decomposition can significantly improve the prediction accuracy of carbon price.

To enhance the scientific nature of risk prevention and control in the supply chain network of new energy vehicle (NEV), and to strengthen safety production and operational management in China’s NEV industry, we integrate complex network theory with SEIR (susceptible-exposed-infectious-recovered) modeling to simulate the process of risk propagation in the NEV supply chain network, aiming to uncover the mechanisms of risk propagation. Firstly, typical NEV companies such as Tesla and XPeng are selected as case studies, with suppliers as nodes and supplier cooperation relationships as edges to construct the topological networks of their automotive supply chains. Secondly, topological parameters such as average degree, clustering coefficient, and average path length are used to explore the characteristics of the supply chain networks of these two companies. Finally, based on the characteristics of the topological networks, an SEIR epidemic model is constructed for the supply chain networks to simulate the impact of different immunization strategies on the speed and scope of risk propagation in the supply chain. The results indicate: 1) The supply chain networks of both NEV companies exhibit scale-free network structures, with comparable network densities (average degrees of 2.293 for Tesla’s and 1.845 for XPeng’s supply chain networks). 2) Comparing the simulation results of risk prevention strategies between the two companies shows that their performances are largely similar. The proposed model effectively explores the characteristics of risk propagation in the NEV supply chain. Specifically, extending the incubation period of risks can significantly slow down the spread of risks, providing nearly three months of adjustment time for the companies, with Tesla experiencing a shorter delay of about 2 weeks to the peak risk period compared to XPeng; Shortening the duration of infection can notably reduce the scale of risk spread by approximately 20%, with Tesla showing a 4% greater reduction in the scope of risk impact compared to XPeng. Additionally, increasing the complexity of the supply chain network may accelerate the propagation of risks. The research findings can provide a reference for NEV companies to formulate effective risk response measures, ensuring the stability and safety of the supply chain.

New quality productive forces is an important engine for high-quality economic development and a crucial force in promoting the great rejuvenation of the Chinese nation in the new era. This article constructs a corresponding indicator evaluation system based on the connotation of the theory of new quality productive forces, and uses the entropy method to calculate the level of new quality productive forces in 236 cities in China from 2010to 2020, by constructing a theoretical model and conducting empirical research, the impact of new quality productive forces on economic growth is explored. The results indicate that new quality productive forces significantly promotes economic growth, and the three dimensions of new quality productive forces have heterogeneous effects on promoting economic growth. Mechanism analysis shows that environmental regulation intensity plays a regulatory role in promoting economic growth through new quality productive forces. In further analysis, by constructing a spatial lag model to test the spatial spillover effect of new quality productive forces, it was found that new quality productive forces can not only promote the macro economy of local cities, but also promote the economic growth of surrounding cities.